The current vignette is a quick analysis of all CRAN packages that have “glm” in their name. The asssumption here is that a package with “glm” in its name most probably does something related to generalized linear models.
Download today’s CRAN database and clean and organise author names, depends, imports, suggests, enhances.
library("cranly")
p_db <- tools::CRAN_package_db()
package_db <- clean_CRAN_db(p_db)
Let’s build the CRAN package directives and collaboration networks
package_network <- build_network(package_db)
author_network <- build_network(package_db, perspective = "author")
The packages that have “glm” in their name are
(glm_packages <- package_with(package_network, name = "glm"))
#> [1] "bestglm" "bglm" "biglm"
#> [4] "brglm" "brglm2" "circglmbayes"
#> [7] "CompGLM" "CPMCGLM" "designGLMM"
#> [10] "dglm" "dhglm" "EBglmnet"
#> [13] "ezglm" "geoRglm" "glm.ddR"
#> [16] "glm.deploy" "glm.predict" "glm2"
#> [19] "GLMaSPU" "glmbb" "glmBfp"
#> [22] "glmc" "glmdm" "glmertree"
#> [25] "glmgraph" "glmlep" "glmm"
#> [28] "glmmADMB" "glmmBUGS" "glmmLasso"
#> [31] "glmmML" "GLMMRR" "glmmsr"
#> [34] "glmmTMB" "glmnet" "glmnetcr"
#> [37] "glmnetUtils" "glmpath" "glmpathcr"
#> [40] "glmtlp" "glmulti" "glmvsd"
#> [43] "glmx" "HBglm" "HDGLM"
#> [46] "hglm" "hglm.data" "HiCglmi"
#> [49] "icdGLM" "lsplsGlm" "mbrglm"
#> [52] "mcemGLM" "mcglm" "MCMCglmm"
#> [55] "mdhglm" "MGLM" "mglmn"
#> [58] "misclassGLM" "mvglmmRank" "oglmx"
#> [61] "pglm" "plsRglm" "poisson.glm.mix"
#> [64] "QGglmm" "r2glmm" "randomGLM"
#> [67] "simglm" "speedglm" "StroupGLMM"
and the corresponding subnetworks are
glm_package_network <- subset(package_network, package = glm_packages)
glm_author_network <- subset(author_network, package = glm_packages)
As the following visualizations illustrate these networks are heavily connected
visualize(glm_package_network, package = glm_packages, title = TRUE)
visualize(glm_author_network, package = glm_packages, title = TRUE)
The top-20 packages in terms of various statistics of the directives sub-network for generalized linear models are
glm_package_summaries <- summary(glm_package_network)
plot(glm_package_summaries, according_to = "degree")
plot(glm_package_summaries, according_to = "betweenness")
plot(glm_package_summaries, according_to = "page_rank")
The top-20 in the collaboration sub-network for generalized linear models are
glm_author_summaries <- summary(glm_author_network)
plot(glm_author_summaries, according_to = "degree")
plot(glm_author_summaries, according_to = "betweenness")
plot(glm_author_summaries, according_to = "page_rank")